Grip strength, mating success, and immune and energy costs in a population sample of US women and men: a registered report
Abstract
Theory and evidence suggest that the mating benefits of muscle mass in human males trade off with costs of increased energy intake and decreased measures of native immunity, likely due to an evolutionary history of sexual selection. It is unknown if females experience a similar tradeoff. Using data from the 2013-2014 phase of the National Health and Nutrition Examination Survey (NHANES), a large nationally representative sample of the US (N = 4384), we will test whether grip strength, a proxy for upper body strength and physical formidability, is a positive predictor of self-reported measures of adult mating success (lifetime number of sexual partners, past year number of sexual partners, age at first intercourse, and current partnership status) in both males and females, controlling for numerous anthropometric, socioeconomic, hormone, health, and physical activity related potential confounds. We will also test if there is a positive relationship between grip strength and dietary energy intake, and a negative relationship between grip strength and native immune function. This study replicates an earlier study of US males (Lassek & Gaulin, 2009) with numerous additional control variables, and it additionally tests these relationships in females.
Sexual dimorphism in modern humans
The mechanisms that have shaped human sexual dimorphism and whether they have primarily operated on male or female traits remain debated. Sexual dimorphism refers to sex differences in morphological and behavioral traits, excluding reproductive organs (Plavcan, 2001). Unlike most animals, mammalian sexual size dimorphism, when it exists, tends to be male biased (Andersson, 1994; Tombak, Hex, & Rubenstein, 2024). In humans, sexual size dimorphism is observable as early as the first trimester in utero where male embryos are larger than female embryos (Bukowski et al., 2007). Body composition dimorphism is evident through childhood such that, adjusted for height, males have higher lean body mass and females have higher fat mass (Kirchengast, 2010). At puberty, sexual dimorphism in size and body composition significantly increases such that in adults, adjusting for a 7-8% dimorphism for height, men have 12-25% higher body mass (Lassek & Gaulin, 2022), which is slightly greater than body mass dimorphism in gibbons, and slightly less than chimpanzees (Plavcan, 2012; Puts, Carrier, & Rogers, 2023; Smith & Jungers, 1997). Men also have larger and stronger bones (Wells, 2007). Other traits that are sexually dimorphic in humans include digit ratio, voice pitch, facial features, body and facial hair growth, and canine length (Puts et al., 2023).
While humans are only moderately dimorphic in terms of overall body mass, this is not the case for fat and muscle allocation, which are highly dimorphic (Puts, 2010) due to women’s copious storage of fat and male investment in muscle mass. Men have between 30% and 42% more fat-free mass (Lassek & Gaulin, 2022), 61% more overall muscle mass, and 78% more muscle mass in the upper arms. This concentrated muscle dimorphism in the arms and back translates to greater upper body strength in men than women. Studies of sex differences in strength show that men are stronger on average than women on all tests of muscle strength, but especially for tests of upper-body muscle strength, where female upper-body strength is 50-60% of male upper-body strength while lower-body strength is 60-70% of male values, and trunk strength is 60% of male values (Nuzzo, 2023). Muscle mass alone does not explain the sex difference in strength, as strength assessments are often greater in males than females even when pair-matched on muscle thickness (Kataoka et al., 2023), and likewise men have greater strength-to-body mass ratios than women (Nuzzo, 2023).
There is no evidence for a sex difference in the ability of the nervous system to drive the muscle (voluntary action) but instead differences in strength lie in muscle characteristics including mass, size, and fiber type. Men have both more absolute muscle mass and greater mass proportional to body size, and these muscles have higher volume and cross-sectional area size. These differences are greatest in the upper body. Finally, a greater proportion of male to female muscle is occupied by type II muscle fibers, which create greater force than type I (Nuzzo, 2023).
Sexual dimorphism hypotheses
Two hypotheses for the evolution of human sexual size dimorphism and body composition dimorphism dominate the literature: 1) intrasexual selection through male contests and 2) intersexual selection through female mate choice. Both assert that men with more masculine traits had higher biological fitness over evolutionary history: intrasexual selection if more formidable men were able to physically outcompete other men for access to mates, and intersexual selection if females preferentially mated with men who displayed masculine traits. The male contest and the female choice hypotheses concur that sexual size and strength dimorphism are proximately (that is, developmentally) caused by sex differences in androgen hormones in the uterine environment and during differentiation at puberty. They differ, however, in the ultimate (functional) explanations for these differences, as discussed below.
Male contest competition
The intrasexual selection hypothesis emphasizes evidence that female mate choice was limited in the ancestral past (Puts, 2010), and that instead male fitness was determined by physical contests with other males for access to mates. Under these circumstances more physically formidable males (i.e., those having the ability to inflict costs on competitors) had higher reproductive success (Hill, Bailey, & Puts, 2017; Plavcan, 2012; Puts, 2016; Puts et al., 2023). Therefore, human males have been sexually selected to be formidable resulting in the male bias in morphological traits like bone density, height, weight, muscle mass, and strength, as well as behavioral traits such as aggression (Archer, 2009; Puts et al., 2023). Particular emphasis is placed on sexual dimorphism in the upper-body due to the important role upper-body strength plays in both armed and unarmed fighting ability (Sell, Hone, & Pound, 2012) and where sex differences in strength and muscularity are greatest as discussed above.
In extant nonhuman primate species, sexual dimorphism in body weight and canine size are strongly associated with the degree of intrasexual competition, a relationship which, if used to infer the behavior of extinct australopithecines, suggests a high degree of intrasexual competition in ancestral species (Plavcan & van Schaik, 1997). Evidence of sexual size dimorphism in Homo is less certain, but in general shows reduced size dimorphism compared to earlier hominin species (Plavcan, 2012).
Female choice for masculine traits
Although there is evidence in Western young adult populations that male mating success is mediated by male-rated dominance and not by female-rated attractiveness (Hill et al., 2013; Kordsmeyer, Hunt, Puts, Ostner, & Penke, 2018), others nevertheless argue that sexual dimorphism in humans is largely the result of female choice for high quality males (which was possibly heavily influenced by parents, in which case parental preferences would also have been important, Apostolou, 2007). The immunocompetence handicap hypothesis, for example, relies on evidence that testosterone is immunosuppressive, and therefore only males with highly competent immune systems can afford to pay the costs of high levels of testosterone required to develop masculine secondary sexual traits (Folstad & Karter, 1992). Androgen-dependent traits could thus be costly signals of good genetic quality, with females increasing their fitness by preferentially mating with males displaying these traits, thereby conferring heritable immunity to their offspring and securing investment from a healthy partner (Folstad & Karter, 1992). Evidence that testosterone is immunosuppressive is mixed, however (Nowak, Pawłowski, Borkowska, Augustyniak, & Drulis-Kawa, 2018), and although testosterone is associated with mating success, it does not seem to be through the mechanism of female choice for testosterone-derived face or body features since testosterone does not predict female rated masculinity or attractiveness (Peters, Simmons, & Rhodes, 2008).
Similar arguments have therefore been proposed that masculine traits could be more general cues of quality and condition, or an ability to bear other costs (Frederick & Haselton, 2007; Kokko, Brooks, Jennions, & Morley, 2003). Cues of upper body strength indeed account for most of the variation in female-rated male body attractiveness (Sell et al., 2017). Yet masculine trait expression is not consistently related to retrospective or prospective health (Boothroyd, Scott, Gray, Coombes, & Pound, 2013).
Females might therefore be choosing males who could provide more resources, or men who could better protect them from attacks by other men or by predators, which in ancestral hunter-gatherer populations would have been physically stronger men (Apicella, 2014; Willems & Schaik, 2017). There is substantial cross-cultural evidence of a female-biased preference for mates who can provide resources (Buss & Schmitt, 2019; Walter et al., 2020). Importantly, studies of contemporary hunter-gatherers and game-theoretic models find that a mutually beneficial sexual division of labor within long-term pair bonds, in which women engage in lower risk, lower return foraging of plants and small game compatible with the cognitively demanding task of childrearing (Hagen & Garfield, 2019), and men engage in higher risk, higher return big game hunting (Kelly, 2013), is critical to provisioning offspring who remain dependent on caregivers for 20 years or more (Alger, Hooper, Cox, Stieglitz, & Kaplan, 2020; Davison & Gurven, 2022; Davison & Gurven, 2021; Kaplan, Hill, Lancaster, & Hurtado, 2000). See Figure 1. This suggests that sex differences in body composition could be due, at least in part, to a sexual division of labor.
More generally, multiple forms of sexual selection could have played a role in the evolution of human sexual dimorphism (see Hill et al., 2017 for a brief review of alternative hypotheses for sexual strength dimorphism).
Strength and reproductive success
A meta-analysis found that whereas voice pitch, height, and testosterone levels were associated with mating success in low fertility populations, only muscularity was associated with actual reproductive success in high fertility populations (Lidborg, Cross, & Boothroyd, 2022). In four studies of convenience samples (two of which included women), hand grip strength was positively related to mating success (including number of sex partners) for men, but there was either no relationship or a negative relationship for women (Gallup, White, & Gallup, 2007; Shoup & Gallup, 2008; Sneade & Furnham, 2016; Varella, Valentova, Pereira, & Bussab, 2014)
The literature on the relationship between grip strength and mating and reproductive success is disproportionately reported for men (Gallup & Fink, 2018). There have been very few tests of the hypotheses that some female traits, such as breast size and waist-hip ratio (WHR), have evolved by sexual selection. A meta-analysis found weak evidence that more feminine digit ratios predicted higher fertility, insufficient evidence for voice pitch, and none regarding facial femininity, breast size or waist-hip ratio (WHR). At present there is mixed evidence that strength is related to reproductive success in women (Lidborg & Boothroyd, 2022). One study among the Himba showed that women with higher strength/muscularity had more living children and grandchildren (Atkinson et al., 2012), while a similar study among the Hadza showed no relationship between strength and reproductive outcomes (Smith, Olkhov, Puts, & Apicella, 2017). The dearth of research on female traits and reproductive outcomes leaves us unable to draw any firm conclusions about selection pressures over time.
Previous research has shown that physical formidability in males (operationalized as fat free mass and limb muscle volume) predicts numbers of total and past-year self-reported sex partners, but that it also involves costs such as increased daily energy intake and decreased immune function, operationalized as C-reactive protein and white blood cell count (Lassek & Gaulin, 2009), consistent with a tradeoff in mating success, immunity, and energy costs. However, these relationships were not tested in women, for whom both sexual selection hypotheses predict no association.
Experimental aims and hypotheses
According to the sexual selection hypothesis, males evolved greater physical formidability than females because physical formidability increased male reproductive success more than females. The aim of this study is to replicate the costs and benefits of formidability reported by Lassek & Gaulin (2009) using similar nationally representative data, controlling for a wider range of possible confounds, and precisely prespecifying the statistical models in a registered report format. It also aims to investigate if these costs and benefits are also experienced by women, and specifically if the effect of physical strength on mating success, a proxy for reproductive success, is greater for men than women, as the sexual selection hypothesis predicts.
Pilot study
In order to refine our hypotheses and statistical models for our confirmatory study, which will use data we have not yet observed, we first conducted a pilot study whose results we report here.
Methods
To assess the relationship between formidability and mating benefits, and immune and energy costs, we used data from the Centers for Disease Control (CDC) National Health and Nutrition Examination Survey (NHANES). NHANES utilizes a complex, multi-stage sampling strategy in order to collect data representative of the civilian, non-institutionalized U.S. population. NHANES combines interview, examination, and laboratory data to assess health status and identify health risks for adults and children in the United States. Data collection occurs in new cycles every two years.
The pilot study uses the 2011-2012 dataset, whereas the 2013-2014 dataset has been held out for confirmatory analysis pending in principle acceptance of this registered report. Grip strength, our proxy for physical formidability and our key predictor variable, was only collected in these years. We include data from US adults between the ages of 18 and 60, the years when grip strength is the most stable (Hogrel, 2015). For each model, participants will be included if they have complete data for each predictor and outcome variable.
Outcome variables
Our main outcome was mating success. In industrialized populations like the U.S., widespread access to contraceptives uncouples reproductive success from sexual behavior. Measures of mating success, including number of sexual partners and age at first sexual intercourse, are used as proxies as they are assumed to have been strongly correlated with male reproductive success under ancestral conditions (Pérusse, 1993). We operationalized mating success in three ways following Lassek & Gaulin (2009): 1) Total sexual partners was a count based on responses to the question: “In your lifetime, with how many men/women have you had any kind of sex?” 2) Total sexual partners in the past year, from the question: “In the past 12 months, with how many men/women have you had any kind of sex?” Because of the way these questions are framed, (e.g. female participants were asked about their male partners and vice versa) these variables represent heterosexual partners (however, it is not necessarily the case that all of the participants included in these models identified as heterosexual). 3) Age at first sexual intercourse reported from: “How old were you when you had sex for the first time?” The sexual behavior questionnaire was self-administered on a computer in a private room at the examination center, using the Audio Computer Assisted Self Interview system, which allows participants to hear questions through headphones as well as read them on screen. Only respondents who could self-report were asked these questions.
Lifetime and past year numbers of sexual partners index success through short term mating strategies. Lifetime numbers are probably a more precise proxy of the use of a short-term mating strategy as these are integrated over the entire life, whereas past year numbers likely fluctuate depending on partnered status and other transient factors. However, both are somewhat ambiguous measures as they could also represent repeated rejection, or a tendency to exaggerate or understate the actual numbers. Neither clearly indexes female reproductive success because multiple sexual partners are likely to have increased male fitness more than female fitness (i.e., the Bateman gradient, Lehtonen, 2022). We therefore also operationalized mating success in a fourth outcome measure as partnered status as a proxy for a long-term mating strategy that, as discussed earlier, benefits both sexes. Partnered status included participants who reported either being married or living with a partner whereas unpartnered included participants who reported being single, widowed, divorced, or separated (Lassek & Gaulin, 2009, included marital status as a control variable but not as an outcome variable).
Our second outcome measure was immune investment, which we operationalized as white blood cell count (1000 cells/µL). Lassek & Gaulin (2009) also used C-reactive protein (CRP) as an outcome variable, but CRP was not measured in the 2011-2014 data collection years and so is not included here.
Finally, to assess costs of greater strength, we investigated the relationship between strength and dietary energy and protein intake. Participants reported all food and beverages they consumed in the 24 hour period prior to their interview. This interview was repeated for a different 24 hour period 3-10 days later to obtain dietary recalls for two separate days. These data were then used to estimate energy intake in the form of kilocalories (kcal) calculated by matching reported foods to the USDA’s Food and Nutrient Database for Dietary Studies. For the outcome measures in these models we used the average number of calories per day and grams of protein per day calculated for each participant across their two recall days.
Key explanatory variables
Our main explanatory variable of mating success is formidability, which Lassek & Gaulin (2009) operationalized as fat-free mass and limb muscle volume. Since those variables were not available in our data, we operationalized formidability as combined grip strength (kg), the sum of the highest of three readings taken on each hand using a dynamometer. All regression models also included sex (male/female), our second main explanatory variable. All models also controlled for age in years and partnered status (not included in models where partnered status was the outcome variable).
The interaction between sex and strength was crucial for our models since we want to test if the effect of strength on mating and immune outcomes is different for women than for men. Grip strength is highly sexually dimorphic, however, with about 90% of men stronger than about 90% of women (Smith, Rosenström, & Hagen, 2022), and is thus highly collinear with sex, a problem for regression modeling and interpretation. We therefore computed a sex-specific grip strength value as follows: for each sex we centered grip strength at the sex-specific mean and divided by two times the sex-specific standard deviation. Thus, for women, low or high values are relative to other women, and for men, low or high values are relative to other men. The sex-specific values were used in all models except the immune function and dietary intake models described below, which assess the costs of high strength. Here, an index of absolute rather than relative strength is called for, and we used grip strength standardized across both sexes.
We also interacted sex with age and partnered status. We interacted age with sex since menopause occurs within the age range of our participants. For models of past year number of sexual partners we included an interaction between partnered status and grip strength since being currently partnered would plausibly impact mating behavior in the past year regardless of strength. Continuous-valued predictor variables were centered and standardized by 2 standard deviations which approximately matches the variation in binary variables like sex, thus making the regression coefficients more comparable (Gelman, 2008).
Models
We attempted to replicate the regression models in Lassek & Gaulin (2009) as closely as possible. There were some unavoidable differences, however. First, our study aimed to investigate the role of sex; thus we include sex as a main effect as well as in interactions with key predictor variables. Second, some variables included in Lassek & Gaulin (2009) were not available in our data. Third, we included additional control variables as noted below. Finally, we treated our pilot study as an exploratory study in which we fit numerous models not reported here. The models we report in the pilot study and which we will test exactly in the confirmatory study represent those that we consider to best test the hypotheses in Lassek & Gaulin (2009) based on the theories we described above, the results of our exploratory analyses, and our constraints.
Control models
Our modeling strategy takes inspiration from Lassek & Gaulin (2009). However, Lassek & Gaulin (2009) utilized stepwise regression to automatically eliminate candidate predictors from their models which has since been found to overfit data and therefore estimated coefficients fail to replicate in future samples (Smith, 2018). Instead, we will fit generalized linear regression models (GLMs) with prespecified treatment and control variables chosen based on theoretical considerations and our exploratory analyses. Control variables included a much wider range of theoretically motivated potential confounds than used by Lassek & Gaulin (2009), organized into themes. We specified five models for each mating success outcome measure in order to determine if the effect of grip strength on those outcomes was due to confounds with socioeconomic, health, hormone, or physical activity variables that have been associated with strength and sexual behavior.
Anthropometric control model
We first derived a simple model based on that reported in Table 2 in Lassek & Gaulin (2009) for each outcome measure (total number of partners, past year number of partners, and age at first intercourse) predicted by age, sex, grip strength, and partnered status. This model also includes an anthropometric control, body mass index (BMI), calculated as \(kg/m^2\), since body size could impact either strength or mating outcomes. We interacted age, strength, and BMI with sex to assess differences in these predictors for men and women.
Socioeconomic control model
The socioeconomic control model included education and race as categorical variables, since there is evidence that race is related to variation in both strength (Johnson & Wilson, 2019) and sexual behavior (Fenton et al., 2005). Likewise, there is also variation in sexual behavior by education (Chandra, Copen, & Mosher, 2013), and there is variation in physical activity by education (He & Baker, 2005).
Health control model
An umbrella review found the grip strength is a useful indicator of general health status, early all-cause mortality, cardiovascular mortality, and disability (Soysal et al., 2021). Furthermore, healthy status is associated with higher likelihood of sexual activity, frequency of sexual activity, and reported quality of sex in men and women (Lindau & Gavrilova, 2010). Another study of men aged 45-59 found that lower sexual activity was associated with increased mortality (Davey Smith, Frankel, & Yarnell, 1997). The health model, therefore, included a number of variables related to health. White blood cell count (1000 cells/µL) and hemoglobin (g/dL) were included to control for acute infection. Depression, which is negatively related to strength (Smith et al., 2022), was measured using the Patient Health Questionnaire-9 (PHQ-9, Kroenke, Spitzer, & Williams, 2001), a validated nine-item screening instrument. Each item represents a symptom of depression, and for each one participants were asked to consider how frequently they had been bothered by that symptom over the past two weeks, rated on a scale from 0 (not at all) to 3 (nearly everyday). These ratings were summed to produce a depression score ranging from 0-27. Chronic illness was included using Chronic Disease Score (0-6) a count of chronic diseases participants reported having been diagnosed with including diabetes, cancer, stroke, arthritis, heart disease and respiratory disease. A point was added for each disease a participant reported being diagnosed with, regardless of any impairment due to the disease. We then controlled for impairment resulting from chronic illness (Disease Impairment Score; 0-5) calculated from a different NHANES question, which asked participants to list up to five health conditions that specifically cause them to have difficulties with physical activities. We also controlled for physical disability and using the item “special equipment” which referred to participants’ report that they needed special equipment to walk. Finally the ‘perceived abnormal weight’ variable was coded as true or false depending on whether a participant reported that they perceived their weight to be abnormal.
Hormone control model
There is evidence that testosterone is positively associated with higher numbers of partners for men and less robustly so for women (Pollet, Meij, Cobey, & Buunk, 2011; Van Anders, Hamilton, & Watson, 2007), but mixed evidence that it is related to sexual desire (Van Anders, 2013). For review of mixed evidence of the often complex relationships between testosterone and sexual desire, sexual behavior, partnered status, and parenting for men and women see Van Anders (2013). Likewise, although some have found a positive correlation between circulating testosterone and handgrip strength (Chiu, Shih, & Chen, 2019), other studies have found no association (Gettler, Agustin, & Kuzawa, 2010; Ribeiro et al., 2016). There is evidence that testosterone-related single nucleotide polymorphisms (SNPs) are associated with greater strength performance (Guilherme et al., 2021). The hormone control model included serum total testosterone (ng/dL). Since male mean testosterone is an order of magnitude higher than the female mean, testosterone, like grip strength, was highly confounded with sex, one of our key predictor variables. We therefore computed a sex-specific testosterone value in the same way we did for strength. This differed from other centered and standardized variables which we centered and standardized across all male and female values.
Physical activity control model
Physical activity is related to sexual behavior and function in adults (Morris, Marshall, & Demers, 2022). Therefore, the physical activity control model included four dichotomous variables of vigorous and moderate work and recreational activity coded as 1 if participants reported that their work and/or recreation caused large increases in heart rate or breathing for at least 10 minutes continuously (vigorous work and/or rec), or small increases in breathing or heart rate for 10 minutes continuously (moderate work and/or rec), and zero if they reported their work and/or recreation did not.
Costs of strength
Following Lassek & Gaulin (2009) we also developed models of native immune function (operationalized as white blood cell count) and dietary energy intake (operationalized as average calories per day). For each outcome we developed a model as similar as possible to those reported in Lassek & Gaulin (2009), except that we always include sex and its interaction with other predictors where we have theoretical reasons to expect sex differences in the effect of the predictor on the outcome. We then also specified an alternative model for each outcome with additional control variables that could confound strength and energy or immune investment. These models were based in part on exploratory analyses of multiple models not reported here.
Native Immune Function
Following the significant predictors reported in Lassek & Gaulin (2009), Table 4, we first specified a replication model with age, sex, strength, and BMI. We also included an age:sex interaction because we expected that immune investment over the lifespan might differ between males and females due to menopause in women. Finally, we included a strength:sex interaction to test the hypothesis that the immune costs of strength differ by sex.
Based on exploratory analysis we developed an expanded control model of WBCC that included a few additional controls. First, we controlled for sex-specific testosterone and an interaction of sex and sex-specific testosterone since testosterone possibly has immunosuppressive effects, which may differ by sex. We also controlled for three variables related to energy availability that could impact immune investment. First, food security status for adults in the participant’s household was assessed using 10 items from the U.S. Food Security Survey Module. The resulting value can range from 1-4, where 1 represents full food security and 4 represents very low food security. Second, the total metabolic equivalent (total MET) was calculated based on participants’ responses to questions about the minutes they spent walking or bicycling, engaging in vigorous and moderate work, and vigorous and moderate recreation per day, using MET scores provided by NHANES. We included dietary energy intake (average calories per day as discussed above). Finally, we substituted separate height and weight variables for BMI.
Dietary Energy and Protein Intake
Based on Lassek & Gaulin (2009), Table 3, we developed a replication model of dietary energy intake (average calories per day) and included age, sex, strength, BMI, and total MET. We did not include interactions with sex in this model because we do not anticipate sex differences in the effects of any predictor variable, nor did we see any in exploratory analyses. We then developed an expanded control model that also controlled for WBCC and food insecurity in addition to the variables in the model above, since either could influence energy intake and strength. Given the protein costs of muscle maintenance we likewise developed two models of dietary protein intake. We based the “replication model” on that of the Lassek & Gaulin (2009) dietary energy model, and then applied the same expanded controls discussed above in the “expanded controls model,” including the substitution of separate height and weight variables for BMI.
A tradeoff between short- and long-term mating
Exploratory analysis revealed a negative association between the lifetime number of sexual partners and the probability of being in a committed relationship (partnered status), which we interpreted as a possible tradeoff between short- and long-term mating strategies. In models of partnered status (one of our mating success outcome variables), we therefore included the lifetime number of sexual partners to assess if this negative association persisted after including the sets of control variables described above.
Analysis
All analyses were completed in R version 4.3.2 (2023-10-31), using the survey package (version 4.1-1, Lumley, 2021) in order to incorporate the survey sampling weights and to preserve the representative structure of the sample. We modeled lifetime and past year numbers of partners using a quasi-Poisson generalized linear regression model (glm), since these variables are overdispersed count data. We modeled age at first sexual intercourse and white blood cell count with a Gaussian glm. We modeled partnered status using a binomial glm. Analyses included adults ages 18-60 because this is the period when the majority of reproduction takes place and because these are the years when grip strength is the most stable.
Pilot Study Results
Our initial analyses found little support for a sex difference in the effect of strength on mating success, contrary to the sexual selection hypothesis. We traced this failure to the unusually high number of lifetime sex partners reported by some men and women. We discovered that whereas women reported a median number of 5 lifetime sex partners, and men 7 partners, there were 82 individuals who reported 100 or more partners, a few of whom reported 1000 or more (Figure 2). We decided to restrict our sample to individuals with fewer than 100 lifetime sex partners for several reasons. First, support for the sexual selection hypothesis was weak if these individuals were included, but much stronger if they were excluded. We suspect that because our sample is large and nationally representative, it probably includes sex workers, as well as individuals who regularly use the services of sex workers, and whose high partner numbers would therefore not reflect mate attraction or intrasexual competition. Second, there was an unusually high number of participants reporting exactly 100 lifetime sex partners, suggesting inaccurate recollections. Finally, our statistical models do not easily accommodate the high degree of overdispersion caused by these few exceptionally high partner numbers, and our extensive simulations showed that high overdispersion would reduce our statistical power to detect sex differences. We will use the same cutoff for our confirmatory study.
Descriptive statistics
The mean age was 38 years. Sexual dimorphism—the ratio of male mean:female mean—was evident in an 8% higher value for male height and a 16% higher value for weight; females, however, had a 1% higher BMI than males on average. There was a much larger degree of male-biased dimorphism in combined grip strength (57%). Hemoglobin was 14% higher in males than females while white blood cell count was 3% higher in women. See Table S1 for weighted means, standard deviations, and standardized mean differences of all variables for men and women.
Coefficients of strength, and strength X sex interaction
Our pilot results revealed strong positive significant main effects of sex-specific strength on partnered status, numbers of lifetime partners, and age at first sex after controlling for all five sets of potential confounds. The only exception was that the strength coefficient for age at first sex was not significant after controlling for socioeconomic factors, although the sign and magnitude was very similar to those of these coefficients in the models with other control variables. There was no significant effect of sex-specific strength on the number of partners in the past year. See the left column of Figure 3.
There were also clear sex differences in the effects of sex-specific strength on two mating success outcomes—partnered status and lifetime number of partners—with smaller effects for women compared to men, as predicted by the sexual selection hypothesis. There was also a persistent negative association of numbers of lifetime partners on the probability of partnered status in all models of this outcome (Figure S2). Finally there was no evidence for a sex difference in the effects of sex-specific strength on past year number of partners or age at first sex, contrary to the sexual selection hypothesis. See the right column of Figure 3.
Immune costs of strength
Previous research found that higher musculature was associated with decreased investment in native immunity. We developed two models of immune investment in white blood cell count (Figure 4). In the replication model, strength was a significant negative predictor of WBCC for both men and women (there was no significant interaction of sex and strength) controlling for age and BMI. In the expanded model with additional controls, including the substitution of height and weight for BMI, strength was now not significant, albeit with a coefficient that was very similar to the replication model.
Models of energy and protein intake
Finally, we tested whether strength was related to increased energy (Figure 5) or protein intake (Figure 6). We found that strength was a significant, positive predictor of both energy and protein intake controlling for sex. In the expanded models with additional controls, strength was no longer a significant predictor of energy intake but remained a significant predictor of protein intake.
Pilot Results Discussion
Using grip strength as a proxy for muscularity, this pilot study replicated findings from Lassek & Gaulin (2009) that muscularity is significantly positively associated with three of the four indices of mating success, including our additional index of mating success—partnered status—even after controlling for a wider range of potential confounds including socioeconomic, health, hormone, and physical activity variables. The only exceptions were that strength was not a significant predictor of past year partners, nor of age at first sex in the socioeconomic control model; the coefficient of the latter, however, was similar in sign and magnitude to the significant strength coefficients in the other models. Strength was also associated with immune, and energy, and protein costs in the replication models, but not in the expanded control models of immune or energy costs; it remained a significant predictor of protein intake, however. The expanded control models included height, which might be a proxy for aspects of strength, such as lower body strength, that are not accounted for by grip strength.
The effect of strength on age at first sex was not significant in our socioeconomic model probably because it is confounded with ethnicity. Specifically, Asian Americans had a markedly lower grip strength compared to the other ethnic groups, and a later age at first sex. In addition, participants, whose grip strength was measured at the time of the study when they were adults, are reporting on an event that happened an average of 22 years in the past when most were adolescents and their grip strength had not yet reached its adult value. For these reasons, we do not predict an effect of strength on age at first sex after controlling for ethnicity.
The sexual selection hypothesis predicts that the effect of strength on mating success will be greater for men than for women, and this prediction was exceptionally well-supported for partnered status and numbers of lifetime partners. In humans, biological fitness depends critically on extensive investment in offspring which typically takes place in long-term partnerships. We therefore propose that the sexual selection hypothesis for the sexual dimorphism in physical formidability applies to long-term partnerships (proxied by our partnered variable), as well as short-term matings (proxied by partner numbers). Thus, our pilot study supports the hypothesis that muscularity increases both short- and long-term mating success for men more than it does for women. Number of lifetime partners was also a negative predictor of partnered status (Figure S2), suggesting a tradeoff in pursuing these two reproductive strategies.
The lack of a sex difference in the significant positive effect of sex-specific strength on short term mating success (proxied by number of partners in the last year) is puzzling. Because men have greater strength than women their self-reported partner numbers are higher, on average, although the median number of partners in the last year for both sexes is 1. Still, women with higher grip strength report more partners in the past year than women with lower grip strength even after controlling for a host of potential confounds. It could be that there was selection for more formidable men to prefer more partner variety, and stronger women have a similar preference as a byproduct of selection on men. It could be that there is assortative mating on strength; thus if stronger men are motivated to switch partners more frequently, their (stronger) mates would also likely have more mates. It might be that stronger women require less male investment and so instead benefit from greater partner numbers through, e.g. genetic bet-hedging, forging relationships with multiple males, ability to conduct a more extensive search for a high quality long term mate, or through avoiding costly long term partnerships. It might also be the case that there are some sex-specific confounds that we failed to control for. Greater partner numbers might indicate mating failures rather than mating successes for males or females, although why strength would be associated with mating failures is not clear. Finally, our simulations revealed that we had relatively lower power to detect a significant interaction between strength and sex for this outcome (see Power section below).
Our pilot results support the importance of strength to male short-term mating success as well as to the long-term partnerships that are the basis of biparental care in human reproduction (Bribiescas, Ellison, & Gray, 2012; Gettler, Boyette, & Rosenbaum, 2020). Across evolutionary time, fitness for both men and women was heavily dependent on successfully obtaining long term partners, thereby reaping the efficiency benefits of a sexual division of labor (Quinlan, 2008; Quinlan & Quinlan, 2007). More specifically, humans require provisioning for an extended period (Davison & Gurven, 2022; Davison & Gurven, 2021) (Figure 1), and paternal care is an important predictor of child outcomes (Starkweather et al., 2021; Winking, Gurven, & Kaplan, 2011). Thus, our pilot study underscores that the sexual selection hypothesis for the relationship between strength and mating success should include long-term mating in addition to numbers of sexual partners. Moreover, because strength indexes productivity in addition to formidability, our results could demonstrate female choice for productivity, i.e., the greater ability over human evolution of stronger men to provision offspring through big-game hunting (Apicella, 2014; Kaplan et al., 2000).
Limitations
Our registered report has one aim: to test the prediction from the sexual selection hypothesis that the effect of upper body strength on reproductive success is significantly positive for males and significantly less so for females. Our aim is not to test all possible byproduct hypotheses, nor is it possible to do so. Therefore, we are unable to control for in-utero testosterone exposure, or other conditions such as Polycystic Ovarian Syndrome (PCOS) that might influence the relationship between grip strength and our measures of mating success. Likewise, NHANES does not collect data on sociosexuality. As described in our methods, in industrialized populations like the U.S., widespread access to contraceptives uncouples reproductive success from mating behavior for both men and women. Measures of mating success, including partnered status, number of sexual partners, and age at first sexual intercourse, are used as proxies as they are assumed to have been strongly correlated with reproductive success under ancestral conditions (Pérusse, 1993). Finally, all pilot results were obtained after considerable exploratory analysis, and must be replicated in a confirmatory study.
Confirmatory Study (Not yet conducted)
In a large, nationally representative US sample, we predict that formidability will be significantly positively associated with mating success as indexed by partnered status and lifetime number of sexual partners, but not last year number of partners, nor age at first sex after controlling for ethnicity. We also predict a significant interaction with sex such that strength will be a stronger predictor of male than female partnered status and number of lifetime partners, but not number of last year partners nor age at first sex. We also predict that there will be a significant negative association between formidability and immune function. Finally, we predict that formidability will be positively associated with dietary protein intake for both males and females.
We indicate our predictions for the results of the confirmatory tests, specifically, our predictions for significant coefficients of strength and of the interaction between sex and strength, in Table 1. We indicate for each outcome whether we predict a significant positive, negative, or no effect of these variables across our control models.
| Outcome | Strength coefficient | Strength X Sex coefficient |
|---|---|---|
| Partnered status | + | - |
| Lifetime partners | + | - |
| Past year partners | 0 | 0 |
| Age at first sex | 0 | 0 |
| Immunity | - | 0 |
| Energy | 0 | |
| Protein | + |
Power
Effects in replication studies are often smaller than in the original studies (Klein et al., 2018). We therefore used simulations to estimate our power to detect strength and sex X strength coefficients that are 25-100% as large as the ones observed in our pilot study, with a sample size equal to that in our unobserved data. We have high power to detect true effects at least 75% as large as the ones observed in our pilot study, and reasonable power to detect those that are at least 50% as large (Table 2)
| Partnered | Lifetime partners | Last year partners | ||||
|---|---|---|---|---|---|---|
| Scale | strength | sex X strength | strength | sex X strength | strength | sex X strength |
| 0.25 | 0.76 | 0.27 | 0.89 | 0.32 | 0.19 | 0.065 |
| 0.50 | 1.00 | 0.77 | 1.00 | 0.83 | 0.59 | 0.102 |
| 0.75 | 1.00 | 0.98 | 1.00 | 0.99 | 0.89 | 0.165 |
| 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.99 | 0.281 |
Method
Our methods and models will remain identical to those reported in the pilot study, using unobserved data from the 2013-2014 NHANES collection cycle.
Acknowledgements
We thank the Editor and two anonymous reviewers for their numerous helpful comments and suggestions.
Data availability
All data and code are available here: https://github.com/grasshoppermouse/strengthmating
Supplementary Information
Pilot study
| Variable | Male | Female | Sexual Dimorphism | |||
|---|---|---|---|---|---|---|
| N | Mean (SD)1 | N | Mean (SD)1 | Ratio | SMD2 | |
| Age at first sex (years) | 1,566 | 17.29 (3.90) | 1,575 | 17.45 (3.46) | 0.99 | -0.05 |
| Lifetime number of sexual partners | 1,684 | 12.03 (15.10) | 1,654 | 7.31 (8.88) | 1.65 | 0.38 |
| Past year number of sexual partners | 1,631 | 1.41 (2.30) | 1,606 | 1.02 (1.00) | 1.38 | 0.22 |
| Combined Grip Strength (kg) | 1,596 | 93.29 (17.00) | 1,526 | 59.28 (10.35) | 1.57 | 2.42 |
| Age (Years) | 1,684 | 38.39 (12.85) | 1,654 | 39.19 (12.54) | 0.98 | -0.06 |
| Body mass index (kg/m^2) | 1,675 | 28.34 (6.05) | 1,633 | 28.99 (7.62) | 0.98 | -0.09 |
| Height (cm) | 1,677 | 176.40 (7.58) | 1,636 | 163.05 (7.08) | 1.08 | 1.82 |
| Weight (kg) | 1,675 | 88.31 (20.25) | 1,633 | 77.04 (20.80) | 1.15 | 0.55 |
| White blood cell count (1000 cells/µL) | 1,628 | 6.92 (2.02) | 1,600 | 7.22 (2.11) | 0.96 | -0.14 |
| Hemoglobin (g/dL) | 1,628 | 15.13 (1.08) | 1,600 | 13.28 (1.16) | 1.14 | 1.65 |
| Testosterone (ng/dL) | 1,571 | 410.86 (171.16) | 1,536 | 26.02 (24.08) | 15.79 | 3.15 |
| Chronic Disease Score (0-6) | 1,529 | 0.40 (0.65) | 1,515 | 0.56 (0.82) | 0.71 | -0.22 |
| Disease Impairment Score (0-5) | 1,684 | 0.19 (0.67) | 1,654 | 0.28 (0.85) | 0.68 | -0.12 |
| Depression Score (0-27) | 1,678 | 2.67 (4.01) | 1,650 | 3.47 (4.52) | 0.77 | -0.19 |
| Dietary energy intake (kcals) | 1,435 | 2,545.36 (844.86) | 1,466 | 1,850.41 (614.10) | 0.94 | |
| Dietary protein intake (grams) | 1,435 | 99.54 (36.89) | 1,466 | 69.82 (25.59) | 1.43 | 0.94 |
| Food Insecurity Rating (1-4) | 1,680 | 1.53 (0.96) | 1,650 | 1.58 (0.99) | 0.97 | -0.05 |
| Total MET | 1,676 | 89.20 (115.46) | 1,651 | 42.38 (67.20) | 2.10 | 0.50 |
| Partnered | 1,537 | 880 (59%) | 1,521 | 828 (61%) | ||
| Education | ||||||
| 1 | 1,684 | 90 (3.9%) | 1,654 | 70 (3.0%) | ||
| 2 | 1,684 | 269 (12%) | 1,654 | 225 (9.9%) | ||
| 3 | 1,684 | 388 (23%) | 1,654 | 303 (17%) | ||
| 4 | 1,684 | 521 (32%) | 1,654 | 581 (36%) | ||
| 5 | 1,684 | 416 (29%) | 1,654 | 475 (35%) | ||
| Race and Ethnicity | ||||||
| MexicanAmerican | 1,684 | 204 (9.9%) | 1,654 | 173 (8.2%) | ||
| OtherHispanic | 1,684 | 154 (6.7%) | 1,654 | 179 (7.2%) | ||
| NonHispanicWhite | 1,684 | 615 (65%) | 1,654 | 559 (64%) | ||
| NonHispanicBlack | 1,684 | 403 (10%) | 1,654 | 479 (14%) | ||
| NonHispanicAsian | 1,684 | 240 (4.8%) | 1,654 | 212 (4.6%) | ||
| OtherRace | 1,684 | 68 (2.8%) | 1,654 | 52 (2.7%) | ||
| Perceived abnormal weight | 1,677 | 807 (51%) | 1,650 | 1,062 (65%) | ||
| Special equipment needed to walk | 1,537 | 62 (2.6%) | 1,521 | 90 (4.5%) | ||
| Work involves vigorous activity | 1,684 | 459 (31%) | 1,654 | 174 (12%) | ||
| Work involves moderate activity | 1,684 | 676 (44%) | 1,654 | 494 (34%) | ||
| Recreation involves vigorous activity | 1,684 | 639 (37%) | 1,654 | 386 (25%) | ||
| Recreation involves moderate activity | 1,684 | 758 (49%) | 1,654 | 756 (51%) | ||
| 1 Weighted means and standard deviations shown for continuous variables, unweighted n (%) for categorical variables | ||||||
| 2 Standardized Mean Difference | ||||||
Models of mating success with anthropometric, socioeconomic, health, hormone, and physical activity controls
The negative association of numbers of lifetime sexual partners on partnered status
Correlation matrices of study variables
Regression tables
Lifetime number of partners
| Anthropometric | Socioeconomic | Health | Activity | Hormone | |
|---|---|---|---|---|---|
| (Intercept) | 2.712 | 2.611 | 2.685 | 2.674 | 2.706 |
| (0.636) | (2.521) | (1.298) | (0.972) | (0.640) | |
| age_centered | 0.535 | 0.472 | 0.482 | 0.530 | 0.539 |
| (1.041) | (1.389) | (1.596) | (1.204) | (1.028) | |
| sexfemale | −0.442 | −0.454 | −0.480 | −0.447 | −0.444 |
| (0.810) | (1.091) | (1.571) | (0.948) | (0.812) | |
| strength_centered | 0.505 | 0.439 | 0.515 | 0.479 | 0.486 |
| (1.003) | (1.333) | (1.495) | (1.126) | (0.989) | |
| partneredTRUE | −0.455 | −0.414 | −0.414 | −0.453 | −0.440 |
| (0.784) | (1.069) | (1.188) | (0.892) | (0.783) | |
| bmi_centered | −0.161 | ||||
| (1.197) | |||||
| age_centered × sexfemale | −0.370 | −0.354 | −0.359 | −0.374 | −0.381 |
| (1.656) | (2.193) | (2.481) | (1.862) | (1.668) | |
| sexfemale × strength_centered | −0.383 | −0.388 | −0.391 | −0.389 | −0.398 |
| (1.638) | (2.144) | (2.403) | (1.827) | (1.614) | |
| sexfemale × bmi_centered | 0.016 | ||||
| (1.704) | |||||
| edu | −0.033 | ||||
| (0.497) | |||||
| raceOtherHispanic | 0.303 | ||||
| (2.832) | |||||
| raceNonHispanicWhite | 0.224 | ||||
| (2.208) | |||||
| raceNonHispanicBlack | 0.364 | ||||
| (2.506) | |||||
| raceNonHispanicAsian | −0.363 | ||||
| (3.996) | |||||
| raceOtherRace | 0.368 | ||||
| (3.516) | |||||
| perceived_abnormal_weightTRUE | −0.107 | ||||
| (1.196) | |||||
| whitebloodcell_centered | 0.053 | ||||
| (1.188) | |||||
| hemoglobin_centered | −0.030 | ||||
| (1.474) | |||||
| special_equipmentTRUE | 0.107 | ||||
| (3.470) | |||||
| chronic_disease_score | 0.062 | ||||
| (0.843) | |||||
| physical_disease_count | 0.024 | ||||
| (0.879) | |||||
| depression | 0.015 | ||||
| (0.137) | |||||
| vigorous_rec | 0.071 | ||||
| (1.003) | |||||
| moderate_rec | 0.075 | ||||
| (0.902) | |||||
| vigorous_work | 0.020 | ||||
| (1.157) | |||||
| moderate_work | −0.060 | ||||
| (1.000) | |||||
| testosterone_sex_centered | 0.212 | ||||
| (0.943) | |||||
| sexfemale × testosterone_sex_centered | −0.082 | ||||
| (1.394) | |||||
| Num.Obs. | 2837 | 2853 | 2742 | 2853 | 2673 |
Past year number of partners
| Anthropometric | Socioeconomic | Health | Activity | Hormone | |
|---|---|---|---|---|---|
| (Intercept) | 0.399 | 0.524 | 0.445 | 0.403 | 0.399 |
| (0.799) | (2.852) | (1.700) | (1.202) | (0.805) | |
| age_centered | −0.385 | −0.458 | −0.469 | −0.456 | −0.453 |
| (1.343) | (1.833) | (2.199) | (1.562) | (1.356) | |
| sexfemale | −0.291 | −0.303 | −0.313 | −0.296 | −0.307 |
| (0.937) | (1.294) | (1.982) | (1.117) | (0.946) | |
| strength_centered | 0.298 | 0.228 | 0.255 | 0.267 | 0.228 |
| (1.495) | (2.058) | (2.457) | (1.728) | (1.540) | |
| partneredTRUE | −0.207 | −0.172 | −0.177 | −0.217 | −0.190 |
| (0.946) | (1.327) | (1.545) | (1.098) | (0.963) | |
| bmi_centered | −0.357 | ||||
| (1.551) | |||||
| age_centered × sexfemale | 0.009 | 0.073 | 0.083 | 0.072 | 0.042 |
| (1.979) | (2.689) | (3.174) | (2.264) | (2.028) | |
| sexfemale × strength_centered | −0.110 | −0.071 | −0.044 | −0.069 | −0.017 |
| (1.885) | (2.523) | (3.005) | (2.141) | (1.893) | |
| strength_centered × partneredTRUE | −0.229 | −0.227 | −0.243 | −0.261 | −0.262 |
| (1.817) | (2.478) | (2.940) | (2.094) | (1.860) | |
| sexfemale × bmi_centered | 0.327 | ||||
| (2.037) | |||||
| edu | −0.068 | ||||
| (0.603) | |||||
| raceOtherHispanic | 0.255 | ||||
| (3.063) | |||||
| raceNonHispanicWhite | 0.081 | ||||
| (2.380) | |||||
| raceNonHispanicBlack | 0.351 | ||||
| (2.741) | |||||
| raceNonHispanicAsian | −0.048 | ||||
| (3.859) | |||||
| raceOtherRace | 0.294 | ||||
| (3.983) | |||||
| perceived_abnormal_weightTRUE | −0.175 | ||||
| (1.520) | |||||
| whitebloodcell_centered | 0.107 | ||||
| (1.501) | |||||
| hemoglobin_centered | −0.020 | ||||
| (1.923) | |||||
| special_equipmentTRUE | 0.015 | ||||
| (4.994) | |||||
| chronic_disease_score | 0.084 | ||||
| (1.139) | |||||
| physical_disease_count | −0.007 | ||||
| (1.303) | |||||
| depression | 0.003 | ||||
| (0.184) | |||||
| vigorous_rec | −0.012 | ||||
| (1.174) | |||||
| moderate_rec | 0.079 | ||||
| (1.076) | |||||
| vigorous_work | 0.153 | ||||
| (1.378) | |||||
| moderate_work | −0.162 | ||||
| (1.211) | |||||
| testosterone_sex_centered | 0.103 | ||||
| (1.184) | |||||
| sexfemale × testosterone_sex_centered | −0.069 | ||||
| (1.744) | |||||
| Num.Obs. | 2748 | 2763 | 2656 | 2763 | 2591 |
Age at first sex
| Anthropometric | Socioeconomic | Health | Activity | Hormone | |
|---|---|---|---|---|---|
| (Intercept) | 17.202 | 15.676 | 17.718 | 17.143 | 17.217 |
| (2.349) | (7.422) | (4.554) | (3.377) | (2.379) | |
| age_centered | 0.753 | 0.899 | 0.884 | 0.700 | 0.587 |
| (3.815) | (4.849) | (5.744) | (4.328) | (3.839) | |
| sexfemale | 0.069 | 0.008 | 0.228 | −0.044 | 0.054 |
| (2.484) | (3.190) | (4.939) | (2.907) | (2.511) | |
| strength_centered | −0.652 | −0.420 | −0.818 | −0.692 | −0.686 |
| (3.685) | (4.680) | (5.476) | (4.112) | (3.691) | |
| partneredTRUE | 0.298 | 0.050 | 0.126 | 0.284 | 0.280 |
| (2.602) | (3.389) | (3.927) | (2.944) | (2.640) | |
| bmi_centered | −0.420 | ||||
| (4.190) | |||||
| age_centered × sexfemale | 0.240 | 0.247 | 0.260 | 0.270 | 0.382 |
| (5.306) | (6.690) | (7.907) | (5.923) | (5.406) | |
| sexfemale × strength_centered | 0.367 | 0.310 | 0.445 | 0.354 | 0.419 |
| (5.154) | (6.486) | (7.616) | (5.732) | (5.170) | |
| sexfemale × bmi_centered | 0.324 | ||||
| (5.291) | |||||
| raceOtherHispanic | −0.646 | ||||
| (8.277) | |||||
| raceNonHispanicWhite | −1.038 | ||||
| (6.068) | |||||
| raceNonHispanicBlack | −1.494 | ||||
| (7.378) | |||||
| raceNonHispanicAsian | 3.020 | ||||
| (9.530) | |||||
| raceOtherRace | −1.373 | ||||
| (10.838) | |||||
| edu | 0.649 | ||||
| (1.551) | |||||
| perceived_abnormal_weightTRUE | −0.015 | ||||
| (3.886) | |||||
| whitebloodcell_centered | −0.611 | ||||
| (3.847) | |||||
| hemoglobin_centered | −0.018 | ||||
| (4.931) | |||||
| special_equipmentTRUE | 0.493 | ||||
| (12.217) | |||||
| chronic_disease_score | −0.414 | ||||
| (2.776) | |||||
| physical_disease_count | −0.011 | ||||
| (3.159) | |||||
| depression | −0.090 | ||||
| (0.478) | |||||
| vigorous_rec | 0.072 | ||||
| (3.254) | |||||
| moderate_rec | 0.410 | ||||
| (2.892) | |||||
| vigorous_work | −0.626 | ||||
| (3.834) | |||||
| moderate_work | 0.085 | ||||
| (3.206) | |||||
| testosterone_sex_centered | −0.536 | ||||
| (3.592) | |||||
| sexfemale × testosterone_sex_centered | 0.442 | ||||
| (4.962) | |||||
| Num.Obs. | 2702 | 2718 | 2619 | 2718 | 2557 |
Partnered status
| Anthropometric | Socioeconomic | Health | Activity | Hormone | |
|---|---|---|---|---|---|
| (Intercept) | 0.774 | 1.139 | 0.743 | 0.726 | 0.774 |
| (1.308) | (4.614) | (2.595) | (1.924) | (1.316) | |
| sex_partners_scaled | −0.322 | −0.296 | −0.294 | −0.322 | −0.315 |
| (0.626) | (0.850) | (0.937) | (0.710) | (0.625) | |
| age_centered | 1.518 | 1.594 | 1.588 | 1.497 | 1.446 |
| (2.368) | (3.226) | (3.645) | (2.704) | (2.378) | |
| sexfemale | −0.093 | −0.048 | −0.102 | −0.129 | −0.079 |
| (1.480) | (2.039) | (2.960) | (1.741) | (1.489) | |
| strength_centered | 1.017 | 1.115 | 0.979 | 1.023 | 1.097 |
| (2.256) | (3.071) | (3.453) | (2.555) | (2.298) | |
| bmi_centered | 0.131 | ||||
| (2.498) | |||||
| age_centered × sexfemale | −0.899 | −0.951 | −1.004 | −0.938 | −0.818 |
| (3.166) | (4.282) | (4.826) | (3.556) | (3.215) | |
| sexfemale × strength_centered | −0.703 | −0.686 | −0.730 | −0.743 | −0.818 |
| (3.058) | (4.125) | (4.641) | (3.437) | (3.089) | |
| sexfemale × bmi_centered | −0.264 | ||||
| (3.106) | |||||
| edu | 0.075 | ||||
| (0.975) | |||||
| raceOtherHispanic | −0.686 | ||||
| (5.186) | |||||
| raceNonHispanicWhite | −0.640 | ||||
| (3.923) | |||||
| raceNonHispanicBlack | −1.603 | ||||
| (4.691) | |||||
| raceNonHispanicAsian | −0.458 | ||||
| (5.899) | |||||
| raceOtherRace | −0.955 | ||||
| (6.888) | |||||
| perceived_abnormal_weightTRUE | 0.295 | ||||
| (2.307) | |||||
| whitebloodcell_centered | −0.117 | ||||
| (2.273) | |||||
| hemoglobin_centered | 0.004 | ||||
| (2.892) | |||||
| special_equipmentTRUE | 0.169 | ||||
| (7.032) | |||||
| chronic_disease_score | 0.046 | ||||
| (1.690) | |||||
| physical_disease_count | −0.257 | ||||
| (1.831) | |||||
| depression | −0.038 | ||||
| (0.276) | |||||
| vigorous_rec | −0.192 | ||||
| (1.892) | |||||
| moderate_rec | 0.242 | ||||
| (1.701) | |||||
| vigorous_work | −0.021 | ||||
| (2.268) | |||||
| moderate_work | 0.027 | ||||
| (1.875) | |||||
| testosterone_sex_centered | −0.483 | ||||
| (2.151) | |||||
| sexfemale × testosterone_sex_centered | 0.710 | ||||
| (3.109) | |||||
| Num.Obs. | 2837 | 2853 | 2742 | 2853 | 2673 |
References
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